Rationale and Research Questions

Background

In the context of escalating global environmental challenges, the shift from traditional fossil fuel-based energy sources to renewable energy has become a focal point in efforts to reduce carbon emissions and combat climate change (Kabeyi et. al, 2022). However, the transition’s broader environmental impacts, particularly on air quality, remain less explored. This transition is especially relevant given the increasing global energy demand and the need to meet this demand sustainably.

Significance

Understanding the relationship between renewable energy generation and air quality is crucial. Renewable energy sources like wind and solar are lauded for their lower environmental impact compared to fossil fuels, which are major contributors to air pollution (UCSUSA, 2018). Air pollution is a significant environmental hazard, affecting human health, ecosystems, and the climate. It is responsible for millions of premature deaths annually and contributes to the occurrence of diseases like asthma, heart disease, and lung cancer (National Geographic, 2023). Therefore, assessing how increased renewable energy generation affects air quality indicators is not just an environmental concern but a public health imperative.

Theoretical Context

The hypothesis underlying this research is that an increase in renewable energy generation leads to a reduction in air pollution. This hypothesis is grounded in the understanding that renewable energy sources, unlike fossil fuels, do not emit pollutants like sulfur dioxide, nitrogen oxides, and particulate matter during electricity generation.

Research Questions

1: What is the relationship between distributed renewable energy generation and the level of air pollution?

This question aims to investigate the correlation between the rise in renewable energy generation and the concentrations of various air pollutants. It seeks to understand whether regions with higher renewable energy output exhibit lower levels of air pollutants.

2: Among air quality indicators (PM10, PM2.5, CO, NO2, and SO2), which display the most significant response to variations in energy generation?

This question delves deeper into identifying which specific pollutants are most responsive to changes in energy generation types. It is crucial for pinpointing the environmental benefits of renewable energy sources and for policy-making aimed at targeted air pollution reduction.

Dataset Information

The exploratory analysis required the combination of air quality and power plant datasets. Air quality data in the analysis was obtained from the United States Environmental Protection Agency while power plant data was obtained from the U.S. Energy Information Administration. The sample years were the two decades namely 2001 - 2021.

Dataset Information for the Sample Period 2001 - 2021
Dataset Source Variables Used
Air Quality Summary Statistics by Criteria Pollutants and Location EPA Air Quality System (AQS) Monthly Mean Ozone and PM2.5
Power Plant Generator Level Capacities and Locations EIA Form EIA-860 Annual Installed Generation Capacity by Fuel Type
Power Plant Monthly Energy Generation EIA Form EIA-923 Monthly Net Generation by Fuel Type

Exploratory Analysis

We began our exploratory analysis by examining if and how solar and wind energy generating capacity, net generation and air quality has changed over time in the contiguous United States. To begin to visualize this, we used the wrangled power plant to visualize both quantitatively and spatially the change in total solar and wind plant installed capacity over the period 2001 - 2021. From the exploratory line plot, we note the three states with the highest cumulative installed capacity include California, Texas and Iowa which have significant growth in installed capacity compared to other states. To visualize the energy generation associated with these installations, we also plotted the annual energy generation from solar and wind over a similar period and noted that the states with the largest installed capacity are also the states with the highest annual energy generation from these renewable sources.

The change in installed solar and wind plants can be also be visualized spatially across the contiguous United States as illustrated below. From the map, we note that the number of installed solar and wind plants increased significantly over the two decades from 2001 - 2021.

Narrowing down on the top three states with the highest growth in installed capacity, we used ggplot and gganimate to visualize the increase and distribution of plants within the state as shown below:

After establishing that installed capacity of solar and wind plants has increased over time in the United States and particularly in California, Texas and Iowa, we began to explore air quality data to establish the changes in the concentrations of key criteria pollutants over time. To facilitate this, we visualized the wrangled air quality data over time for key pollutants related to fossil fuel generation including SO2, NOX, and PM2.5. Based on the outputs shown below, it can be observed that the amounts of pollutants measured has been trending downwards over time.

Analysis

Question 1:

What is the relationship between distributed renewable energy generation and the level of air pollution?

We can formulate a null and alternative hypothesis for the above research question as follows: H0: There is no change in recorded air quality with an increase in renewable energy generation in the states of California, Texas and Iowa over the period 2001 - 2021. Ha: There is a change in recorded air quality with an increase in renewable energy generation in the states of California, Texas and Iowa over the period 2001 - 2021.

To evaluate this hypothesis, we generated a plot of Mean PM2.5 measured against net monthly solar and wind generation for all three states.

The figures above suggest that the measured value of the pollutants has an inverse relationship or negative correlation with net generation from solar. This implies that the higher the amount of energy generated from wind and solar, the lower the amount of the three criteria pollutants. To investigate this further, we performed a simple linear regression of the relationship between the mean quantity of each pollutant and net energy generation with the results summarized in the table outlined below:

Simple Linear Regression Results

Characteristic PM2.5 SO2 NO2
Beta 95% CI1 p-value Beta 95% CI1 p-value Beta 95% CI1 p-value
California
Net Generation -0.77 -1.1, -0.46 <0.001 -0.21 -0.24, -0.18 <0.001 -1.8 -2.1, -1.6 <0.001
R² 0.088

0.445

0.464

AIC 1,375

189

1,255

σ 3.68

0.349

2.89

Texas
Net Generation -0.28 -0.35, -0.20 <0.001 -0.09 -0.12, -0.07 <0.001 -0.47 -0.57, -0.37 <0.001
R² 0.177

0.213

0.255

AIC 1,000

401

1,148

σ 1.75

0.532

2.34

Iowa
Net Generation -1.3 -1.6, -0.98 <0.001 -0.64 -0.74, -0.55 <0.001 -1.5 -1.8, -1.2 <0.001
R² 0.214

0.423

0.352

AIC 1,161

563

1,053

σ 2.41

0.733

1.99

1 CI = Confidence Interval

Multiple Linear Regression Results

Characteristic PM2.5 SO2 NO2
Beta 95% CI1 p-value Beta 95% CI1 p-value Beta 95% CI1 p-value
California
Net Generation -0.29 -0.82, 0.25 0.3 0.07 0.04, 0.10 <0.001 -1.7 -2.1, -1.2 <0.001
Month 0.27 0.15, 0.40 <0.001 0.01 0.00, 0.01 0.11 0.18 0.08, 0.28 <0.001
Year -0.14 -0.27, -0.01 0.033 -0.08 -0.09, -0.08 <0.001 -0.04 -0.15, 0.06 0.4
R² 0.163

0.818

0.490

AIC 1,357

-87.6

1,246

σ 3.53

0.201

2.83

Texas
Net Generation -0.10 -0.30, 0.11 0.4 0.21 0.17, 0.26 <0.001 0.40 0.15, 0.64 0.002
Month -0.01 -0.07, 0.05 0.8 -0.01 -0.03, 0.00 0.10 0.04 -0.04, 0.11 0.4
Year -0.09 -0.19, 0.00 0.060 -0.16 -0.18, -0.14 <0.001 -0.45 -0.57, -0.33 <0.001
R² 0.189

0.567

0.389

AIC 1,000

254

1,102

σ 1.74

0.396

2.13

Iowa
Net Generation -0.72 -1.4, -0.03 0.041 0.13 -0.06, 0.31 0.2 0.14 -0.40, 0.67 0.6
Month -0.14 -0.23, -0.06 0.001 -0.02 -0.04, 0.01 0.2 -0.09 -0.16, -0.03 0.007
Year -0.10 -0.21, 0.01 0.080 -0.14 -0.17, -0.11 <0.001 -0.30 -0.38, -0.21 <0.001
R² 0.254

0.566

0.462

AIC 1,152

495

1,011

σ 2.35

0.638

1.82

1 CI = Confidence Interval

Question 2:

Among three air quality indicators (PM2.5, NO2, and SO2), which display the most significant response to variations in energy generation?

To evaluate the above research question, we plotted the distribution of quantity of the pollutants over time as demonstrated in the box plots below.

# Interpretation of the Boxplots #it may be redundant, we can decide if we want to keep it or not..

PM2.5 Levels: The distribution of PM2.5 levels varies widely between states. California shows a particularly high range of PM2.5 concentrations with notable outliers, indicating episodes of very poor air quality. It’s important to look into the reasons for California’s variability, such as wildfires or urban pollution.

NO2 Levels: NO2 levels are somewhat variable, with California showing a higher median concentration. This could be associated with industrial activities or high traffic density.

SO2 Levels: The distribution of SO2 is quite tight in most states, except for a few outliers. This pollutant is often associated with industrial processes and the burning of sulfur-containing fuels.

Interpretation

The line graph above displays the trend of renewable energy generation over time in the top 10 states. We can see that there is a general upward trend in renewable energy generation across all states, indicating increased adoption and capacity over time. California (CA) stands out with a significantly higher generation, especially with a steep increase around 2020. Other states also show growth in renewable energy generation but to varying degrees. For instance, Texas (TX) and Iowa (IA) show notable increases. The variability in generation over time could be influenced by factors like state policies, technological advancements, and investment in renewable energy infrastructure.The overall increasing trend aligns with global efforts to transition to cleaner energy sources to reduce reliance on fossil fuels and combat climate change.

#Interpretation

PM2.5 and Net Generation: There doesn’t seem to be a clear relationship between net generation and PM2.5 levels. While some states with higher net generation have lower PM2.5 levels, the data is scattered, suggesting other factors may be at play in determining PM2.5 concentrations.

PM10 and Net Generation: Similar to PM2.5, the PM10 concentrations do not show a clear trend in relation to net generation. There is significant scatter across all levels of net generation.

Sulfur Dioxide (SO2) and Net Generation: The plot for SO2 shows a dense clustering of lower SO2 levels at higher net generation, which might indicate that higher renewable energy generation could be associated with lower SO2 concentrations.

Nitrogen Dioxide (NO2) and Net Generation: The scatter plot for NO2 presents a wide distribution of pollutant concentrations across the net generation axis, indicating no strong correlation between the two.

Carbon Monoxide (CO) and Net Generation: The CO levels are spread across the generation axis, but there is a noticeable cluster of lower CO concentrations at higher levels of net generation.

So, in conclusion, upon reviewing the scatter plots for PM2.5, PM10, NO2, and SO2 in relation to renewable energy generation, it is the scatter plots for Sulfur Dioxide (SO2) and Carbon Monoxide (CO) that show some indication of a relationship. For SO2, higher levels of renewable energy generation seem to correspond with a clustering of lower pollutant concentrations. Similarly, the scatter plot for CO also shows a clustering of data points towards lower CO levels as net generation increases. These observations suggest an inverse relationship where increased renewable energy generation could be associated with decreased emissions of SO2 and CO, which are pollutants typically associated with the combustion of fossil fuels.

To investigate the trend in the measured pollutants over time further, we undertook a time series analysis of the measured values of each of the three pollutants in the three states. Here, we sought to confirm if PM2.5, SO2 and NO2 concentrations changed over the period 2001 - 2021 in California, Texas and Iowa. The null hypothesis is that there has been no change in the recorded PM2.5, SO2 and NO2 concentrations in the three states over the sample period. The alternative hypothesis is that there has been a change in the recorded PM2.5, SO2 and NO2 concentrations in the three states over the sample period.

After decomposing the time series, we noted that each of the three pollutants has a seasonal component as observed in the time series plots shown below. Hence we run the seasonal Mann-Kendall (SMK) test on each dataset, which resulted in a p-value of less than 0.05 (<0.05) for each time series as observed in the results below. This analysis implies that the results are statistically significant and we can reject the null hypothesis. Therefore there has been a change in the recorded PM2.5, SO2 and NO2 concentrations over the period 2001 - 2021 in California, Texas and Iowa. The negative tau values suggests a negative correlation which implies that the change for each pollutant in each state has been a decrease.

## tau = -0.42, 2-sided pvalue =< 2.22e-16
## tau = -0.753, 2-sided pvalue =< 2.22e-16
## tau = -0.729, 2-sided pvalue =< 2.22e-16
## tau = -0.389, 2-sided pvalue =< 2.22e-16
## tau = -0.488, 2-sided pvalue =< 2.22e-16
## tau = -0.715, 2-sided pvalue =< 2.22e-16
## tau = -0.424, 2-sided pvalue =< 2.22e-16
## tau = -0.598, 2-sided pvalue =< 2.22e-16
## tau = -0.672, 2-sided pvalue =< 2.22e-16

Question 1:

What is the relationship between distributed renewable energy generation and the level of air pollution?

Question 2:

Among air quality indicators (PM10, PM2.5, CO, NO2, and SO2), which display the most significant response to variations in energy generation?

Summary and Conclusions

References

Kabeyi, Moses Jeremiah Barasa, and Oludolapo Akanni Olanrewaju. ‘Sustainable Energy Transition for Renewable and Low Carbon Grid Electricity Generation and Supply’. Frontiers in Energy Research, vol. 9, 2022. Frontiers, https://www.frontiersin.org/articles/10.3389/fenrg.2021.743114.

Environmental Impacts of Renewable Energy Technologies | Union of Concerned Scientists. https://www.ucsusa.org/resources/environmental-impacts-renewable-energy-technologies. Accessed 6 Dec. 2023.

Air Pollution. https://education.nationalgeographic.org/resource/air-pollution. Accessed 6 Dec. 2023.